Recent advances in the study of epigenetics and human diseases have prompted a substantial increase in research on the extent to which DNA methylation mediates the effects of environmental exposures on health outcomes. With new and advancing technologies, epigenome-wide DNA methylation data (EWAS) have become the standard for large, population-based epigenetic studies while simultaneously introducing new methodological challenges unique to the use of high-dimensional data in mediation analyses. Due to these challenges, previous studies on epigenetic-mediated effects were generally forced to focus on candidate (gene-specific) methylation markers. More systematic research into this area will remain stalled until the urgent need for analytical methods appropriate and specific to EWAS mediation analysis is met. We propose to develop and apply new analytical approaches to high-dimensional mediation analysis methods for epigenome-wide DNA methylation studies. We will integrate high-dimensional screening and model selection methods into the traditional mediation analysis framework. We will also address sophisticated practical issues in conducting this type of data analysis, specifically: grouping effects of DNA methylation markers on health outcomes, and the generally high correlation among methylation markers. Finally, we will compile and disseminate a user-friendly software package to facilitate implementation of the proposed methods. We will test our proposed methods using data from the Coronary Artery Risk Development in Young Adults Study (CARDIA), a large longitudinal study with available EWAS data that examines the risk factors, development, and determinants of clinical and subclinical cardiovascular disease. Our discoveries may lead to new insights into the mechanisms by which DNA methylation mediates the effects of environmental exposures on health outcomes. It may also inform the development of new interventions targeting these methylation markers and improving the prevention and treatment of disease.